Systems biological analysis of epidermal growth factor receptor internalization dynamics for altered receptor levels

Epidermal growth factor (EGF) receptor (EGFR) overexpression is a hallmark of many cancers. EGF Rendocytosis is a critical step in signal attenuation, raising the question of how receptor expression levels affect the internalization process. Here we combined quantitative experimental and mathematical modeling approaches to investigate the role of the EGFR expression level on the rate of receptor internalization. Using tetramethyl-rhodamine-labeled EGF, we established assays for quantifying EGF-triggered EGFR internalization by both high resolution confocal microscopy and flow cytometry. We determined that the flow cytometry approach was more sensitive for examining large populations of cells. Mathematical modeling was used to investigate the relationship between EGF internalization kinetics, EGFR expression, and internalization machinery. We predicted that the standard parameter used to assess internalization kinetics, the temporal evolution r(t) of the ratio of internalized versus surface-located ligand-receptor complexes, does not describe a straight line, as proposed previously. Instead, a convex or concave curve occurs depending on whether initial receptor numbers or internalization adaptors are limiting the uptake reaction, respectively. To test model predictions, we measured EGF-EGFR binding and internalization in cells expressing different levels of green fluorescent protein-EGFR. As expected, surface binding of rhodamine-labeled EGF increased with green fluorescent protein-EGFR expression level. Unexpectedly, internalization of ligand-receptor complexes increased linearly with increasing receptor expression level, suggesting that receptors and not internalization adaptors were limiting the uptake in our experimental model. Finally, determining the ratio of internalized versus surface-located ligand-receptor complexes for this cell line confirmed that it follows a convex curve, supporting our model predictions.